Table of Contents

Multicodes

Set up

library(gtsummary)
library(tidyverse)
library(srvyr)
library(flextable)
library(officer)
library(labelled)
library(rlang)

Typical theme for gtsummary()

# For all I use the compact theme.
theme_gtsummary_compact()

## Setting theme "Compact"

Load Round 10 UK European Social Survey dataset.

ddir <- paste0(getwd())
load(paste0(ddir,"/ESS_UK_snippet_Round 10.RData"))
glimpse(ess10)

## Rows: 1,149
## Columns: 39
## $ idno         <dbl> 50002, 50008, 50021, 50023, 50033, 50052, 50100, 50129, 50191, 50242, 50297, 50344, 50347, 50378, …
## $ anweight     <dbl> 2.215882, 4.085495, 3.973622, 8.731745, 3.137663, 2.000416, 4.206257, 1.966309, 1.802065, 6.647645…
## $ stratum      <dbl> 1463, 1428, 1501, 1478, 1450, 1456, 1412, 1393, 1491, 1465, 1372, 1473, 1398, 1430, 1410, 1380, 14…
## $ psu          <dbl> 10279, 10236, 10327, 10300, 10387, 10102, 10263, 10413, 10150, 10115, 10208, 10287, 10118, 10401, …
## $ atchctr      <fct> 7, Very emotionally attached, 7, 4, 7, 6, 2, 8, 8, Not at all emotionally attached, 9, Very emotio…
## $ stflife      <fct> 8, 8, 7, 8, 5, 6, 6, 8, 8, 8, Extremely satisfied, 9, 7, 7, 6, 5, 8, 7, 7, 4, 5, Extremely satisfi…
## $ stfgov       <fct> 2, 7, 4, 4, 3, Extremely dissatisfied, 3, 5, 1, Extremely dissatisfied, 5, 5, 3, 2, 1, 4, 8, 5, 1,…
## $ psppsgva     <fct> Very little, Some, Very little, Some, Very little, Very little, Very little, Very little, Very lit…
## $ actrolga     <fct> Quite able, Not at all able, Not at all able, A little able, Not at all able, A little able, Quite…
## $ cptppola     <fct> Not at all confident, Not at all confident, Not at all confident, A little confident, A little con…
## $ lrscale      <fct> 5, 9, 5, 2, 6, 5, 6, 4, 4, 4, 5, 4, 2, 2, 3, 5, 6, 5, 2, 5, 5, 7, 3, NA, 3, NA, NA, 6, 4, 8, 5, 5,…
## $ gincdif      <fct> Disagree, Disagree, Neither agree nor disagree, Agree, Agree, Agree, Agree strongly, Agree, Agree …
## $ ppltrst      <fct> 7, 8, 4, 4, 6, 2, 6, 9, 7, 6, 4, 4, 7, 4, 7, 5, 6, 5, 8, 5, 4, 5, 8, 5, 6, 5, 5, 7, 8, 3, 3, 8, 8,…
## $ trstplt      <fct> 2, 7, 3, 2, No trust at all, 1, No trust at all, 6, 4, 7, 3, 1, 5, 2, 5, 3, 7, 5, 2, 5, No trust a…
## $ imwbcnt      <fct> 7, 3, 6, Better place to live, 4, 5, 9, 5, 7, 9, 7, 6, 8, 8, 8, 5, 5, 9, 9, 5, 6, 3, 8, 8, 6, 8, W…
## $ sclmeet      <fct> Once a month, Several times a month, Less than once a month, Several times a week, Once a month, N…
## $ gndr         <fct> Male, Female, Male, Female, Female, Female, Female, Male, Female, Male, Male, Female, Female, Male…
## $ agea         <dbl> 72, 83, 69, 21, 64, 70, 51, 86, 41, 57, 62, 67, 72, 19, 46, 37, 55, 73, 27, 28, 52, 15, 70, 44, 76…
## $ hincfel      <fct> Living comfortably on present income, Living comfortably on present income, Coping on present inco…
## $ ctzcntr      <fct> Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes,…
## $ brncntr      <fct> Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes,…
## $ feethngr     <fct> Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Yes, No, Yes, Yes, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, …
## $ rlgblg       <fct> No, No, Yes, No, No, Yes, No, No, No, No, No, No, No, Yes, No, No, Yes, No, No, No, No, No, Yes, Y…
## $ rlgblge      <fct> No, Yes, NA, Yes, No, NA, No, No, No, Yes, No, Yes, No, NA, No, No, NA, No, Yes, No, No, Yes, NA, …
## $ vote         <fct> Yes, Yes, Yes, No, Yes, No, Yes, Yes, Yes, Yes, Yes, Yes, Yes, Not eligible to vote, Yes, Yes, Yes…
## $ prtvtdgb     <fct> Labour, Conservative, Conservative, NA, NA, NA, Liberal Democrat, Labour, Labour, Scottish Nationa…
## $ mainact      <fct> NA, NA, NA, NA, NA, NA, NA, NA, NA, "Retired", NA, "Paid work", NA, "Education", NA, "Paid work", …
## $ mnactic      <fct> "Retired", "Retired", "Retired", "Education", "Paid work", "Retired", "Paid work", "Retired", "Pai…
## $ atchctr_num  <dbl> 7, 10, 7, 4, 7, 6, 2, 8, 8, 0, 9, 10, 10, 4, 4, 8, 10, 10, 5, 10, 7, 5, 10, 8, 2, 5, 10, 3, 7, 8, …
## $ stflife_num  <dbl> 8, 8, 7, 8, 5, 6, 6, 8, 8, 8, 10, 9, 7, 7, 6, 5, 8, 7, 7, 4, 5, 10, 9, 7, 3, 3, 10, 2, 4, 8, 6, 5,…
## $ stfgov_num   <dbl> 2, 7, 4, 4, 3, 0, 3, 5, 1, 0, 5, 5, 3, 2, 1, 4, 8, 5, 1, 5, 0, 7, 3, 7, 0, 2, 3, 1, 8, 7, 3, 2, 4,…
## $ psppsgva_num <dbl> 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 4, 4, 3, 2, 3, 2, 3, 2, 2, 3, 3, 1, 2, 1, 2, 2, 2, 4,…
## $ actrolga_num <dbl> 3, 1, 1, 2, 1, 2, 3, 4, 3, 3, 2, 1, 2, 1, 2, 4, 4, 3, 2, 1, 1, 3, 3, 1, 2, 2, 2, 1, 1, 2, 2, 3, 4,…
## $ cptppola_num <dbl> 1, 1, 1, 2, 2, 1, 4, 4, 4, 4, 3, 1, 3, 1, 3, 3, 4, 2, 3, 1, 1, 3, 3, 2, 2, 5, 2, 1, 1, 4, 1, 4, 4,…
## $ lrscale_num  <dbl> 5, 9, 5, 2, 6, 5, 6, 4, 4, 4, 5, 4, 2, 2, 3, 5, 6, 5, 2, 5, 5, 7, 3, NA, 3, NA, NA, 6, 4, 8, 5, 5,…
## $ gincdif_num  <dbl> 4, 4, 3, 2, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 4, 3, 3, 1, 5, 2, 1, 3, 3, 1, 3, 2,…
## $ ppltrst_num  <dbl> 7, 8, 4, 4, 6, 2, 6, 9, 7, 6, 4, 4, 7, 4, 7, 5, 6, 5, 8, 5, 4, 5, 8, 5, 6, 5, 5, 7, 8, 3, 3, 8, 8,…
## $ trstplt_num  <dbl> 2, 7, 3, 2, 0, 1, 0, 6, 4, 7, 3, 1, 5, 2, 5, 3, 7, 5, 2, 5, 0, 5, 6, 4, 1, 0, 0, 0, 3, 2, 2, 3, 5,…
## $ imwbcnt_num  <dbl> 7, 3, 6, 10, 4, 5, 9, 5, 7, 9, 7, 6, 8, 8, 8, 5, 5, 9, 9, 5, 6, 3, 8, 8, 6, 8, 0, 3, 2, 7, 5, 7, 9…

svy_df <- ess10 %>% as_survey_design(weights = anweight)

# List all .R files in ddir 
file_paths <- list.files(path = ddir, pattern = "\\.R$", full.names = TRUE, all.files = T) %>% 
  map_chr(., ~str_remove(.x, ddir)) %>% 
  grep("_interim", ., value =T, invert = T) # Drop interim functions, not used.

# Set of functions to be loaded. 
file_paths

##  [1] "/.col_width.R"             "/addh.R"                   "/base_information.R"       "/bases_equal.R"           
##  [5] "/create_bases.R"           "/gtsummary_table_notes.R"  "/mtotals.R"                "/multicodes.R"            
##  [9] "/nest_multicodes.R"        "/nested_tables.R"          "/prepare_base_for_table.R" "/revise_labels.R"         
## [13] "/splitter_present.R"       "/style_hux_df.R"           "/tables.R"                 "/totals.R"

Load functions

# Load all functions (make sure no other .R files in this location)
map(file_paths, ~source(paste0(ddir,.x)))

Load additional binary data

The code below loads some additional data, for use in multicoded tables.

load("/Users/joecrowley/R/Data/ESS_UK/ESS_UK_R_data_file.RData")

# Use set of variables on location people can access internet from
ess %>% select(starts_with("acc")) %>% map_df(.,var_label) %>% pivot_longer(cols = everything())

name	value
accdng	Feel accomplishment from what I do
accalaw	Acceptable for country to have a strong leader above the law
acchome	Location able to access the internet: Home
accwrk	Location able to access the internet: Workplace
accmove	Location able to access the internet: On the move
accoth	Location able to access the internet: Some other place
accnone	Location able to access the internet: None of the above
accref	Location able to access the internet: Refusal
accdk	Location able to access the internet: Don't know
accna	Location able to access the internet: No answer

# Identify variable names
int_loc <- ess %>% select(starts_with("acc")) %>% names
int_loc <- int_loc[3:10]
int_loc

## [1] "acchome" "accwrk"  "accmove" "accoth"  "accnone" "accref"  "accdk"   "accna"

ess %>% select(all_of(int_loc)) %>% map_df(.,var_label) %>% pivot_longer(cols = everything())

name	value
acchome	Location able to access the internet: Home
accwrk	Location able to access the internet: Workplace
accmove	Location able to access the internet: On the move
accoth	Location able to access the internet: Some other place
accnone	Location able to access the internet: None of the above
accref	Location able to access the internet: Refusal
accdk	Location able to access the internet: Don't know
accna	Location able to access the internet: No answer

ess10 %>% select(ctzcntr, brncntr,feethngr) %>% look_for() %>% convert_list_columns_to_character() %>% flextable

pos	variable	label	col_type	missing	levels	value_labels
1	ctzcntr	Citizen of country	fct	1	Yes; No; Refusal; Don't know; No answer
2	brncntr	Born in country	fct	1	Yes; No; Refusal; Don't know; No answer
3	feethngr	Feel part of same race or ethnic group as most people in country	fct	9	Yes; No; Refusal; Don't know; No answer

Overview of process

Some checks

• all have same base size?
• contains splitter in variable labels? (: )
  
• not contains multiple splitters.

Steps.

1. Checks above,
   
2. Relabel variables, store stem
3. Create unweighted base variable,
   
4. Create vector of variables to use,
5. Create table,
6. Optional, add caption,
7. Optional, add CI.

Proposed variables testing multicodes

ess %>% select(all_of(int_loc)) %>% look_for(details = "none")

pos	variable	label
1	acchome	Location able to access the internet: Home
2	accwrk	Location able to access the internet: Workplace
3	accmove	Location able to access the internet: On the move
4	accoth	Location able to access the internet: Some other place
5	accnone	Location able to access the internet: None of the above
6	accref	Location able to access the internet: Refusal
7	accdk	Location able to access the internet: Don't know
8	accna	Location able to access the internet: No answer

Checks

Bases equal

unique_base_sizes <- 
  ess %>% 
  select(all_of(int_loc)) %>% 
  map_int(., ~sum(is.na(.x))) %>% 
  unique()

unique_base_sizes

## [1] 19830

length(unique_base_sizes) == 1

## [1] TRUE

# As function, returns TRUE if equal

bases_equal <- function(
    df, # data frame
    vars # character vector of variables
    ) {
  
  unique_base_sizes <- 
  df %>% 
  select(all_of(vars)) %>% 
  map_int(., ~sum(is.na(.x))) %>% 
  unique()

unique_base_sizes

result <- length(unique_base_sizes) == 1

return(result)

}

bases_equal(ess, int_loc)

## [1] TRUE

writeLines(
  c("bases_equal <- ", # Assignment needs to be added manually
  deparse(bases_equal)), # Extract function code
  paste0(ddir,"/bases_equal.R")) # File name is just the function name 

Labels contain splitter

I combine checks 2 and 3 in the below

labels_contain_splitter <- 
  ess %>% 
  select(all_of(int_loc)) %>%
  var_label(unlist = T, null_action = "fill") %>% 
  map_int(., ~str_count(.x, ": "))

labels_contain_splitter

## acchome  accwrk accmove  accoth accnone  accref   accdk   accna 
##       1       1       1       1       1       1       1       1

# Check each label contains the splitter
length(labels_contain_splitter) == sum(labels_contain_splitter)

## [1] TRUE

# Check no label contains the splitter more than once
max(labels_contain_splitter) == 1

## [1] TRUE

# As a function, returns TRUE if all contain splitter
splitter_present <- function(
    df, 
    vars) { 
  
  labels_contain_splitter <- 
    df %>% 
    select(all_of(vars)) %>%
    var_label(unlist = T, null_action = "fill") %>% 
    map_int(., ~str_count(.x, ": "))

  labels_contain_splitter
  
  # Check each label contains the splitter, returns TRUE if they do
  all_contain_splitter <- length(labels_contain_splitter) == sum(labels_contain_splitter)
  
  # Check no label contains the splitter more than once, returns TRUE all equal 1
  splitter_appears_once_each <- max(labels_contain_splitter) == 1
  
  # result is TRUE if both statements equal true. 
  result <- all_contain_splitter == TRUE & splitter_appears_once_each == TRUE
  return(result)

}

splitter_present(ess, int_loc)

## [1] TRUE

writeLines(
  c("splitter_present <- ", # Assignment needs to be added manually
  deparse(splitter_present)), # Extract function code
  paste0(ddir,"/splitter_present.R")) # File name is just the function name 

Split variable labels and store stem

# Testing data frame
ess2 <- ess

# Create a list containing the revised variables
revised_labels_as_list <- 
  map(int_loc, function(xxx) { 
  variable_label <- ess %>% select(all_of(xxx)) %>% var_label %>% str_split_i(., ": ", 2)
  return(variable_label)
}) 
names(revised_labels_as_list) <- int_loc # add names

# Revise variable labels
var_label(ess2) <- revised_labels_as_list

# Review corrected labels...
ess2 %>% 
  select(all_of(int_loc)) %>%
  var_label(unlist = T, null_action = "fill")

##             acchome              accwrk             accmove              accoth             accnone              accref 
##              "Home"         "Workplace"       "On the move"  "Some other place" "None of the above"           "Refusal" 
##               accdk               accna 
##        "Don't know"         "No answer"

# Extract stem - I won't make this a function, it's very simple.
ess %>% pull(int_loc[1]) %>% var_label() %>% str_split_i(., ": ", 1)

## [1] "Location able to access the internet"

# As a function - returns relabelled data frame... 
revise_labels <- function(
    df, # data frame
    vars # character vector of variable names
    ) {
  
  # Create a list containing the revised variables
  revised_labels_as_list <- 
    map(vars, function(xxx) { 
    variable_label <- df %>% select(all_of(xxx)) %>% var_label %>% str_split_i(., ": ", 2)
    return(variable_label)
  }) 
  names(revised_labels_as_list) <- vars # add names

  # Revise variable labels
  var_label(df) <- revised_labels_as_list
  return(df)
  
}

# Apply function
ess2 <- revise_labels(ess, int_loc)

# Review corrected labels...
ess2 %>% 
  select(all_of(int_loc)) %>%
  var_label(unlist = T, null_action = "fill")

##             acchome              accwrk             accmove              accoth             accnone              accref 
##              "Home"         "Workplace"       "On the move"  "Some other place" "None of the above"           "Refusal" 
##               accdk               accna 
##        "Don't know"         "No answer"

writeLines(
  c("revise_labels <- ", # Assignment needs to be added manually
  deparse(revise_labels)), # Extract function code
  paste0(ddir,"/revise_labels.R")) # File name is just the function name 

Data set up

These functions are going to expect well managed data.

You can provide variables which are either:

• yes / no.
  
• 0 / 1.
  
• TRUE / FALSE

All variables must have those levels, even if not observed for a particular variable.

# Variables must be factors, drop unused levels..
ess3 <- ess %>% 
  mutate(across(all_of(int_loc), ~fct_drop(to_factor(.x))))

# gtsummary expects yes/no, 0/1 or TRUE/FALSE for dichotomous variables
ess3$acchome %>% levels # we have 'Not marked' / 'Marked'

## [1] "Not marked" "Marked"

ess3 <- ess3 %>% 
  mutate(across(all_of(int_loc), ~fct_recode(.x, 
        "Yes" = "Marked", "No" = "Not marked")))

## Warning: There were 3 warnings in `mutate()`.
## The first warning was:
## ℹ In argument: `across(all_of(int_loc), ~fct_recode(.x, Yes = "Marked", No = "Not marked"))`.
## Caused by warning:
## ! Unknown levels in `f`: Marked
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 2 remaining warnings.

ess3$acchome %>% levels

## [1] "No"  "Yes"

# Note they do all need to have the two levels...
# When adding factor levels remember to use forcats to preserve variable labels
# If done with factor() variable labels will be dropped. 
ess3 %>% select(all_of(int_loc)) %>% map(., levels)

## $acchome
## [1] "No"  "Yes"
## 
## $accwrk
## [1] "No"  "Yes"
## 
## $accmove
## [1] "No"  "Yes"
## 
## $accoth
## [1] "No"  "Yes"
## 
## $accnone
## [1] "No"  "Yes"
## 
## $accref
## [1] "No"
## 
## $accdk
## [1] "No"
## 
## $accna
## [1] "No"

ess3 <- ess3 %>% mutate(across(all_of(int_loc), ~fct_expand(.x, c("Yes", "No")))) 
ess3 %>%  select(all_of(int_loc)) %>% map(., levels)

## $acchome
## [1] "No"  "Yes"
## 
## $accwrk
## [1] "No"  "Yes"
## 
## $accmove
## [1] "No"  "Yes"
## 
## $accoth
## [1] "No"  "Yes"
## 
## $accnone
## [1] "No"  "Yes"
## 
## $accref
## [1] "No"  "Yes"
## 
## $accdk
## [1] "No"  "Yes"
## 
## $accna
## [1] "No"  "Yes"

var_label(ess %>% select(all_of(int_loc)))

## $acchome
## [1] "Location able to access the internet: Home"
## 
## $accwrk
## [1] "Location able to access the internet: Workplace"
## 
## $accmove
## [1] "Location able to access the internet: On the move"
## 
## $accoth
## [1] "Location able to access the internet: Some other place"
## 
## $accnone
## [1] "Location able to access the internet: None of the above"
## 
## $accref
## [1] "Location able to access the internet: Refusal"
## 
## $accdk
## [1] "Location able to access the internet: Don't know"
## 
## $accna
## [1] "Location able to access the internet: No answer"

# Age
ess3 %>% count(age)

age	n
15	1
15.0799999237061	1
15.1700000762939	3
15.25	1
15.3299999237061	1
15.4200000762939	1
15.5	1
15.5799999237061	3
15.6700000762939	1
15.75	4
15.8299999237061	1
15.9200000762939	4
16	1
16.0799999237061	1
16.1700000762939	1
16.25	3
16.3299999237061	4
16.4200000762939	1
16.5	3
16.5799999237061	2
16.6700000762939	3
16.75	1
16.8299999237061	4
16.9200000762939	2
17.1700000762939	1
17.25	3
17.4200000762939	3
17.5	8
17.5799999237061	2
17.6700000762939	4
17.75	4
17.8299999237061	2
18.0799999237061	6
18.1700000762939	1
18.25	2
18.3299999237061	3
18.4200000762939	1
18.5	2
18.5799999237061	4
18.6700000762939	1
18.75	2
18.8299999237061	1
18.9200000762939	1
19	1
19.0799999237061	1
19.1700000762939	2
19.25	2
19.3299999237061	2
19.4200000762939	3
19.5	3
19.5799999237061	2
19.6700000762939	1
19.75	1
19.8299999237061	3
19.9200000762939	3
20	7
20.0799999237061	2
20.1700000762939	1
20.25	1
20.3299999237061	2
20.4200000762939	3
20.5	1
20.5799999237061	3
20.6700000762939	3
20.75	2
20.8299999237061	4
20.9200000762939	4
21	3
21.0799999237061	2
21.1700000762939	4
21.25	2
21.4200000762939	4
21.5	1
21.5799999237061	5
21.8299999237061	5
21.9200000762939	1
22	1
22.0799999237061	1
22.1700000762939	4
22.25	2
22.3299999237061	1
22.4200000762939	2
22.5	2
22.5799999237061	2
22.6700000762939	2
22.75	1
22.8299999237061	1
23.0799999237061	2
23.1700000762939	3
23.25	2
23.3299999237061	1
23.4200000762939	2
23.5	2
23.5799999237061	4
23.6700000762939	4
23.8299999237061	4
23.9200000762939	5
24	1
24.0799999237061	2
24.1700000762939	6
24.25	2
24.3299999237061	4
24.4200000762939	1
24.5	1
24.5799999237061	2
24.6700000762939	8
24.8299999237061	1
24.9200000762939	5
25.0799999237061	4
25.1700000762939	7
25.25	1
25.3299999237061	3
25.4200000762939	2
25.5	1
25.5799999237061	3
25.6700000762939	2
25.75	3
25.9200000762939	5
26	5
26.0799999237061	1
26.1700000762939	4
26.25	3
26.3299999237061	1
26.4200000762939	3
26.5	6
26.5799999237061	2
26.6700000762939	2
26.75	3
26.8299999237061	1
26.9200000762939	1
27	4
27.0799999237061	2
27.1700000762939	1
27.25	8
27.3299999237061	2
27.4200000762939	3
27.5	2
27.5799999237061	2
27.6700000762939	1
27.75	2
27.8299999237061	1
28	1
28.0799999237061	4
28.1700000762939	5
28.3299999237061	4
28.4200000762939	1
28.5	3
28.5799999237061	1
28.75	1
28.8299999237061	3
28.9200000762939	2
29	6
29.1700000762939	3
29.25	2
29.3299999237061	2
29.4200000762939	4
29.5	1
29.5799999237061	5
29.6700000762939	4
29.75	5
29.8299999237061	3
29.9200000762939	3
30	3
30.0799999237061	2
30.1700000762939	3
30.25	3
30.3299999237061	2
30.4200000762939	3
30.5	3
30.5799999237061	4
30.6700000762939	3
30.75	8
30.8299999237061	5
30.9200000762939	2
31	4
31.1700000762939	1
31.25	3
31.3299999237061	2
31.4200000762939	5
31.5	1
31.5799999237061	2
31.6700000762939	3
31.75	2
31.8299999237061	2
31.9200000762939	3
32	4
32.0800018310547	2
32.1699981689453	3
32.25	2
32.3300018310547	1
32.4199981689453	4
32.5	5
32.5800018310547	3
32.6699981689453	3
32.75	2
32.8300018310547	3
32.9199981689453	8
33	7
33.0800018310547	1
33.1699981689453	5
33.25	3
33.3300018310547	2
33.4199981689453	4
33.5	5
33.5800018310547	4
33.6699981689453	7
33.75	1
33.8300018310547	4
33.9199981689453	1
34	4
34.0800018310547	5
34.1699981689453	2
34.25	3
34.3300018310547	2
34.4199981689453	3
34.5	1
34.5800018310547	4
34.6699981689453	1
34.75	3
34.8300018310547	6
34.9199981689453	5
35	7
35.0800018310547	3
35.1699981689453	4
35.25	4
35.3300018310547	7
35.4199981689453	1
35.5	3
35.5800018310547	2
35.6699981689453	2
35.75	2
35.8300018310547	3
35.9199981689453	2
36	7
36.0800018310547	2
36.1699981689453	6
36.25	5
36.3300018310547	3
36.4199981689453	3
36.5	4
36.5800018310547	5
36.6699981689453	2
36.75	3
36.8300018310547	1
36.9199981689453	3
37	1
37.0800018310547	8
37.1699981689453	6
37.25	5
37.3300018310547	5
37.5	5
37.5800018310547	3
37.6699981689453	5
37.75	3
37.8300018310547	6
37.9199981689453	3
38	5
38.0800018310547	6
38.1699981689453	2
38.25	4
38.3300018310547	2
38.4199981689453	2
38.5	1
38.5800018310547	7
38.6699981689453	1
38.75	2
38.8300018310547	4
38.9199981689453	3
39	3
39.0800018310547	6
39.1699981689453	5
39.25	3
39.3300018310547	3
39.4199981689453	7
39.5	2
39.5800018310547	6
39.6699981689453	4
39.75	2
39.8300018310547	2
39.9199981689453	3
40	2
40.0800018310547	4
40.1699981689453	2
40.25	4
40.3300018310547	3
40.4199981689453	4
40.5	3
40.5800018310547	2
40.6699981689453	8
40.75	7
40.8300018310547	1
40.9199981689453	8
41	6
41.0800018310547	5
41.1699981689453	1
41.25	3
41.3300018310547	1
41.4199981689453	8
41.5	6
41.5800018310547	6
41.6699981689453	3
41.8300018310547	3
42	2
42.0800018310547	5
42.1699981689453	4
42.25	4
42.3300018310547	11
42.4199981689453	3
42.5	4
42.5800018310547	6
42.6699981689453	1
42.75	3
42.8300018310547	3
42.9199981689453	3
43	4
43.0800018310547	3
43.1699981689453	4
43.25	3
43.3300018310547	4
43.4199981689453	1
43.5	2
43.5800018310547	6
43.6699981689453	2
43.75	2
43.8300018310547	3
44	4
44.1699981689453	5
44.25	3
44.3300018310547	4
44.4199981689453	6
44.5	6
44.5800018310547	5
44.6699981689453	8
44.75	3
44.8300018310547	5
44.9199981689453	4
45	2
45.0800018310547	5
45.1699981689453	1
45.25	2
45.3300018310547	4
45.4199981689453	3
45.5	8
45.5800018310547	3
45.6699981689453	3
45.75	6
45.8300018310547	5
46	2
46.0800018310547	2
46.1699981689453	3
46.25	4
46.3300018310547	3
46.4199981689453	4
46.5	2
46.5800018310547	6
46.6699981689453	3
46.75	1
46.8300018310547	2
46.9199981689453	2
47	1
47.0800018310547	1
47.1699981689453	3
47.25	2
47.3300018310547	3
47.4199981689453	2
47.5	1
47.5800018310547	2
47.6699981689453	3
47.75	4
47.8300018310547	7
47.9199981689453	5
48	2
48.1699981689453	2
48.25	5
48.3300018310547	3
48.4199981689453	3
48.5	5
48.5800018310547	6
48.6699981689453	4
48.75	3
48.8300018310547	3
48.9199981689453	5
49	5
49.0800018310547	1
49.1699981689453	4
49.25	3
49.3300018310547	2
49.4199981689453	2
49.5	4
49.5800018310547	4
49.6699981689453	6
49.75	4
49.8300018310547	2
49.9199981689453	2
50	4
50.0800018310547	2
50.1699981689453	4
50.25	1
50.3300018310547	4
50.4199981689453	5
50.5	2
50.5800018310547	2
50.6699981689453	4
50.75	3
50.8300018310547	1
50.9199981689453	1
51	3
51.0800018310547	1
51.1699981689453	5
51.25	1
51.3300018310547	3
51.5	5
51.5800018310547	3
51.6699981689453	2
51.75	3
51.8300018310547	1
51.9199981689453	8
52	1
52.0800018310547	5
52.1699981689453	2
52.25	5
52.3300018310547	1
52.4199981689453	5
52.5	2
52.5800018310547	4
52.6699981689453	5
52.75	3
52.8300018310547	2
52.9199981689453	1
53	3
53.0800018310547	4
53.1699981689453	4
53.25	3
53.3300018310547	4
53.4199981689453	2
53.5800018310547	2
53.6699981689453	1
53.9199981689453	1
54	2
54.0800018310547	5
54.1699981689453	4
54.25	2
54.3300018310547	1
54.4199981689453	7
54.5	4
54.5800018310547	4
54.6699981689453	1
54.75	6
54.8300018310547	2
54.9199981689453	3
55	2
55.0800018310547	5
55.1699981689453	2
55.25	2
55.4199981689453	2
55.5	3
55.5800018310547	2
55.6699981689453	2
55.75	1
55.8300018310547	4
55.9199981689453	4
56	5
56.0800018310547	1
56.1699981689453	1
56.25	4
56.3300018310547	2
56.4199981689453	5
56.5	2
56.5800018310547	3
56.75	2
56.8300018310547	4
56.9199981689453	6
57	3
57.0800018310547	2
57.1699981689453	5
57.25	4
57.3300018310547	3
57.4199981689453	7
57.5	2
57.5800018310547	5
57.6699981689453	2
57.75	8
57.8300018310547	2
57.9199981689453	4
58.0800018310547	3
58.1699981689453	4
58.25	5
58.3300018310547	2
58.4199981689453	2
58.5	3
58.5800018310547	2
58.6699981689453	4
58.75	1
58.8300018310547	8
58.9199981689453	2
59	2
59.0800018310547	2
59.1699981689453	3
59.25	8
59.3300018310547	4
59.4199981689453	7
59.5	7
59.5800018310547	5
59.6699981689453	3
59.75	8
59.8300018310547	4
59.9199981689453	2
60	5
60.0800018310547	1
60.1699981689453	8
60.25	2
60.3300018310547	3
60.4199981689453	5
60.5	1
60.5800018310547	5
60.6699981689453	1
60.75	2
60.8300018310547	2
60.9199981689453	4
61	2
61.1699981689453	3
61.25	7
61.3300018310547	4
61.4199981689453	5
61.5	5
61.5800018310547	5
61.6699981689453	7
61.75	4
61.8300018310547	1
61.9199981689453	5
62	1
62.0800018310547	1
62.1699981689453	2
62.25	5
62.3300018310547	3
62.4199981689453	4
62.5	4
62.5800018310547	5
62.6699981689453	2
62.75	1
62.8300018310547	3
62.9199981689453	1
63	2
63.0800018310547	1
63.1699981689453	4
63.25	7
63.3300018310547	6
63.4199981689453	5
63.5	3
63.5800018310547	3
63.6699981689453	4
63.75	4
63.8300018310547	1
63.9199981689453	1
64	3
64.0800018310547	2
64.1699981689453	1
64.25	4
64.3300018310547	1
64.4199981689453	3
64.5	4
64.5800018310547	2
64.6699981689453	3
64.75	3
64.8300018310547	2
64.9199981689453	3
65	3
65.0800018310547	1
65.1699981689453	2
65.25	2
65.3300018310547	2
65.4199981689453	3
65.5	3
65.5800018310547	1
65.6699981689453	3
65.75	2
65.8300018310547	2
65.9199981689453	2
66	4
66.0800018310547	4
66.1699981689453	4
66.25	7
66.3300018310547	1
66.4199981689453	5
66.5	5
66.5800018310547	6
66.6699981689453	4
66.75	2
66.8300018310547	2
66.9199981689453	1
67	8
67.0800018310547	3
67.1699981689453	2
67.25	1
67.3300018310547	2
67.4199981689453	3
67.5	3
67.5800018310547	4
67.6699981689453	4
67.75	2
67.8300018310547	2
67.9199981689453	3
68.0800018310547	2
68.1699981689453	2
68.25	2
68.3300018310547	2
68.4199981689453	2
68.5	5
68.5800018310547	4
68.6699981689453	2
68.75	5
68.9199981689453	2
69	1
69.0800018310547	3
69.1699981689453	4
69.25	2
69.3300018310547	2
69.4199981689453	2
69.5	4
69.5800018310547	3
69.6699981689453	3
69.75	2
69.8300018310547	2
69.9199981689453	1
70	2
70.0800018310547	2
70.1699981689453	2
70.3300018310547	5
70.4199981689453	2
70.5	2
70.5800018310547	2
70.6699981689453	3
70.75	4
70.8300018310547	2
70.9199981689453	3
71	1
71.0800018310547	1
71.1699981689453	3
71.25	2
71.3300018310547	4
71.4199981689453	1
71.5	1
71.5800018310547	2
71.6699981689453	5
71.75	2
71.8300018310547	2
71.9199981689453	2
72	1
72.0800018310547	3
72.1699981689453	5
72.25	2
72.3300018310547	3
72.4199981689453	3
72.5	2
72.5800018310547	1
72.6699981689453	2
72.75	4
72.8300018310547	6
73	1
73.0800018310547	3
73.1699981689453	2
73.25	1
73.3300018310547	4
73.5	3
73.5800018310547	3
73.6699981689453	4
73.75	3
73.8300018310547	4
73.9199981689453	3
74	2
74.0800018310547	2
74.1699981689453	1
74.25	2
74.3300018310547	3
74.4199981689453	1
74.5	1
74.5800018310547	2
74.6699981689453	1
74.75	2
74.9199981689453	4
75	2
75.0800018310547	7
75.1699981689453	3
75.25	4
75.3300018310547	4
75.4199981689453	2
75.5	4
75.5800018310547	4
75.6699981689453	2
75.75	1
75.8300018310547	3
76	1
76.0800018310547	3
76.1699981689453	2
76.25	2
76.3300018310547	3
76.5800018310547	1
76.6699981689453	2
76.75	4
76.8300018310547	1
76.9199981689453	2
77.0800018310547	1
77.1699981689453	1
77.25	2
77.3300018310547	1
77.4199981689453	3
77.5	3
77.5800018310547	2
77.6699981689453	1
77.75	1
77.8300018310547	3
77.9199981689453	3
78	2
78.1699981689453	2
78.25	2
78.3300018310547	2
78.4199981689453	2
78.5	1
78.5800018310547	2
78.6699981689453	1
78.75	1
78.8300018310547	3
78.9199981689453	1
79	2
79.1699981689453	1
79.25	3
79.3300018310547	1
79.4199981689453	2
79.5	2
79.5800018310547	2
79.6699981689453	1
79.75	1
79.8300018310547	2
80.0800018310547	1
80.25	1
80.3300018310547	1
80.4199981689453	1
80.5	3
80.6699981689453	1
80.75	4
80.8300018310547	2
80.9199981689453	2
81	1
81.0800018310547	2
81.1699981689453	3
81.25	2
81.3300018310547	2
81.4199981689453	2
81.5	2
81.5800018310547	3
81.6699981689453	1
81.75	1
81.9199981689453	1
82	2
82.0800018310547	2
82.1699981689453	3
82.25	2
82.3300018310547	3
82.4199981689453	3
82.5	1
82.5800018310547	1
82.6699981689453	5
82.75	1
82.8300018310547	1
82.9199981689453	1
83	3
83.0800018310547	1
83.1699981689453	3
83.25	2
83.3300018310547	3
83.4199981689453	2
83.5	1
83.5800018310547	1
83.6699981689453	4
83.9199981689453	1
84	1
84.0800018310547	1
84.1699981689453	2
84.25	1
84.3300018310547	1
84.4199981689453	1
84.6699981689453	1
84.75	1
84.8300018310547	1
85	1
85.0800018310547	1
85.25	5
85.5	4
85.5800018310547	2
85.6699981689453	1
85.8300018310547	1
85.9199981689453	1
86	3
86.0800018310547	1
86.25	1
86.3300018310547	1
86.4199981689453	1
86.5	3
86.5800018310547	1
86.6699981689453	3
86.75	1
86.9199981689453	2
87	2
87.0800018310547	1
87.1699981689453	2
87.3300018310547	2
87.5	3
87.5800018310547	1
87.9199981689453	1
88	2
88.0800018310547	1
88.25	1
88.4199981689453	1
88.5	1
88.75	1
89.3300018310547	1
89.6699981689453	1
90	1
90.25	1
90.3300018310547	1
90.6699981689453	1
90.75	2
90.8300018310547	1
91.5	1
91.5800018310547	1
91.6699981689453	1
92.0800018310547	1
92.5800018310547	1
92.9199981689453	1
93.1699981689453	1
94.9199981689453	1
96.75	1
	18592

sum(is.na(ess3$age))

## [1] 18592

ess3 <- ess3 %>% mutate(age30 = fct_drop(to_factor(ageyo30)))
ess3 %>% count(age30)

age30	n
29 or under	404
30 or older	1938
	18637

# Gender
ess3 %>% count(gndr)

gndr	n
1	9402
2	11565
	12

sum(is.na(ess3$gndr))

## [1] 12

ess3 <- ess3 %>% mutate(gndr2 = fct_drop(to_factor(gndr)))
ess3 %>% count(gndr2)

gndr2	n
Male	9402
Female	11565
	12

# Ethnicity
ess3 %>% select(contains("pray"))

pray
7
7
6
4
6
7
6
1
1
5
6
1
2
6
6
3
6
7
1
1
4
1
1
7
4
1
1
4
1
7
2
4
7
1
7
7
3
7
7
6
7
1
7
1
7
3
7
6
7
1
2
1
6
7
4
6
7
1
7
7
7
7
6
7
7
7
4
6
7
1
6
7
1
7
6
7
5
6
6
7
7
1
1
1
7
4
1
6
7
1
1
6
3
7
1
4
7
7
4
7
1
7
1
7
7
7
7
1
7
7
7
7
4
1
4
3
7
7
1
7
2
1
1
4
7
3
1
1
1
7
1
6
1
7
7
6
7
3
7
6
6
2
7
7
1
7
1
6
3
3
7
7
7
6
1
1
6
7
1
1
6
7
4
1
7
6
7
1
7
1
3
7
7
7
7
7
7
6
7
1
1
7
7
7
7
7
2
1
7
7
7
7
4
7
1
3
1
1
7
6
7
1
5
7
7
3
1
7
7
4
7
7
7
7
7
6
7
7
3
7
1
3
5
5
7
4
7
1
3
1
1
4
6
7
6
7
3
3
2
7
7
7
7
1
7
7
7
7
4
3
3
1
6
7
7
7
1
6
1
1
2
6
1
1
2
4
7
7
2
1
7
7
7
7
2
4
7
5
7
1
7
1
6
1
7
1
4
7
6
7
1
6
6
6
1
7
7
1
1
1
4
6
4
7
6
1
7
5
1
7
1
7
7
6
7
7
7
6
7
4
7
1
7
7
6
1
1
2
4
1
7
7
7
1
7
7
2
7
3
6
7
3
1
7
2
6
1
7
7
7
1
7
1
7
6
1
2
6
7
4
7
4
3
6
7
7
6
6
6
6
6
7
7
1
7
6
7
7
6
1
7
7
7
4
7
6
7
7
7
7
7
7
3
1
2
7
6
1
4
6
7
7
1
7
4
7
7
6
7
6
1
7
7
6
1
3
4
1
1
3
6
1
7
1
7
6
1
6
7
7
7
7
4
7
6
1
7
2
7
7
2
3
6
6
6
2
7
7
6
1
1
2
4
2
7
7
7
1
7
6
7
1
6
7
2
6
4
7
3
1
7
6
7
6
1
4
4
2
6
7
6
6
4
7
7
6
6
6
1
1
2
1
7
7
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ess3 <- ess3 %>% mutate(pray2 = fct_drop(to_factor(pray)))
ess3 %>% count(pray2)

pray2	n
Every day	3924
More than once a week	1349
Once a week	1113
At least once a month	1151
Only on special holy days	378
Less often	3184
Never	9771
	109

# Multicodes
svy_df <- ess3 %>% filter(!is.na(anweight)) %>% as_survey_design(weights = anweight)

The main function

Applying the function itself.

multicodes <- function(
    df, # data frame 
    vars, # vars
    ci = FALSE, # Confidence intervals, set to TRUE
    caption = NULL, # Replace with caption string
    breaks = NULL, # character vector of crossbreaks
    sig = F, 
    bases = NULL, # The object with base descriptions (from base_information() function)
    source = NULL, # A string describing the data source
    footnotes = NULL # Other character strings as footnotes, each on its own line.
) {
  
  # Check whether survey data or not (T = survey; F = data frame)
  whether_survey_data <- if(
     class(df) %>% 
      grep("survey.design", ., value = T) %>% 
      length() > 0
  ){T} else {F}
  
  # Create object that is always the data frame, not a survey design object
  dataset_reference <- if(whether_survey_data == F){df} else {df[["variables"]]}
  
  # Check variable labels contain ": " and that the base of each variable is equal
  chk1 <- splitter_present(dataset_reference, vars)
  chk2 <- bases_equal(dataset_reference, vars)
  
  # If both checks are TRUE create table
  if (chk1 == TRUE & chk2 == TRUE) {

  # Add variable reflecting number of non-missing cases
  dataset_reference <- dataset_reference %>% 
    mutate(unweighted_base = case_when(
      !is.na(df %>% pull(vars[1])) ~ "Unweighted bases", 
      TRUE ~ NA)
      )
  var_label(dataset_reference$unweighted_base) <- ""

  # Add unweighted base variable to vector of outcomes
  var_set <- c(vars, "unweighted_base")
  
  # Extract variable labels
  variable_label_stem <- dataset_reference %>% pull(vars[1]) %>% var_label() %>% str_split_i(., ": ", 1)

  # Remove stem from variable labels
  dataset_reference <- revise_labels(dataset_reference, vars = vars)
  
  # Put data back into original object
  if(whether_survey_data == T){
    df2 <- df
    df2[["variables"]] <- dataset_reference
  } else {
    df2 <- dataset_reference
  }
  
  # Choose gtsummary function to use
  if(whether_survey_data == T){summary_fun <- tbl_svysummary} else {summary_fun <- tbl_summary}
  
  # Define base size statistic to use
  if(whether_survey_data == T){n_statistic <- "{n_unweighted}"} else {n_statistic <- "{n}"}
  
  # Create table
  tbl <- df2 %>%
    summary_fun(
      include = all_of(var_set),
      type = list(unweighted_base ~ "categorical"),
      missing = "no", 
      statistic = list(all_dichotomous() ~ "{p}", 
                        all_categorical() ~ "{p}",
                        unweighted_base ~ n_statistic), 
      digits = list(all_dichotomous() ~ 1)
    ) 
  
    # Confidence intervals. 
  if (ci == TRUE) {
    tbl <- tbl %>% 
      add_ci(
       include = -unweighted_base,
       statistic = list(all_categorical() ~ "[{conf.low} - {conf.high}]"),
       style_fun = all_categorical() ~ label_style_sigfig(scale = 100, digits = 1)
       )
    }
  
  if(!is.null(breaks)){
    suppressWarnings({suppressMessages({
    
     brk_tbls <- map(breaks, 
        ~df2 %>%
          filter(!is.na(.data[[.x]])) %>%
          summary_fun(
            include = all_of(var_set),
            by = all_of(.x),
            type = list(unweighted_base ~ "categorical"),
            missing = "no", 
            statistic = list(all_dichotomous() ~ "{p}", 
                              all_categorical() ~ "{p}",
                              unweighted_base ~ n_statistic), 
            digits = list(all_dichotomous() ~ 1)
          ))
  
    if(sig == TRUE){
      
      brk_tbls <- map(brk_tbls, 
                      ~.x %>% 
          add_p(include = !all_of("unweighted_base"))
        )
    } 
     
    # Confidence intervals. 
    if (ci == TRUE) {
      brk_tbls <- map(brk_tbls, 
        ~.x %>% 
          add_ci(
           include = -unweighted_base,
           statistic = list(all_categorical() ~ "[{conf.low} - {conf.high}]"),
           style_fun = all_categorical() ~ label_style_sigfig(scale = 100, digits = 1)
           )
        )
    }
    
    brk_tbls <- map2(brk_tbls, breaks, 
        ~.x %>%
          modify_spanning_header(
            all_stat_cols() ~ paste0("**",var_label(dataset_reference[[.y]]),"**"),
            # Added to ensure CI + p.value included in spanning headers
            starts_with("ci_stat") | contains("p.value") ~ paste0("**",var_label(dataset_reference[[.y]]),"**"), 
            ) 
        )
     
     tbl <- append(list(tbl), brk_tbls) %>% tbl_merge(tab_spanner = FALSE)
     
    })})
    
  }
  
  # Edit caption when crossbreaks present. 
  if(!is.null(breaks) & is.null(caption)){
      caption <- paste0("Crosstabulation of ", variable_label_stem)
  } else if (is.null(caption)){
    caption <- ""
    }
  
  tbl <- tbl %>%
    
    # Set headers
    modify_header(all_stat_cols() ~ "**{level}**", 
                  label ~ paste0("**",variable_label_stem,"**"), 
                  # stat_0 ~ "**%**"
                  ) %>% 
    
    # Add a caption
    modify_caption(paste0("<div style='text-align: left; font-weight: bold; color: black'>", 
                        caption,
                        "</div>")) 
  
    # Footnote - whether weighted or unweighted
    if(whether_survey_data == T){
           stat_types <- "All statistics shown in this table are weighted."
         } else {stat_types <- "All statistics shown in this table are unweighted."
      }

    tbl <- tbl %>%
      modify_footnote(all_stat_cols() ~ NA) %>% # Suppress standard footnotes
      modify_footnote_header(columns = "label", stat_types, replace = F)

  # Simplify column header when there are no crossbreaks
  if (is.null(breaks)){tbl <- tbl %>% modify_header(starts_with("stat_0") ~ "**%**")}
    
  if(!is.null(breaks)){
    bases_variables <- c("multicode_labels", breaks)
  }
   
    
    if(!is.null(bases)) {
      
      description_of_multicode_variables <- bases[[2]] %>% unique
      names(description_of_multicode_variables) <- "multicode_labels"
      label_of_multicode_variables <- variable_label_stem
      names(label_of_multicode_variables) <- "multicode_labels"

      bases[[2]] <- c(bases[[2]], description_of_multicode_variables)
      bases[[3]] <- c(bases[[3]], label_of_multicode_variables)
      bases[[4]] <- c(bases[[4]], label_of_multicode_variables)
      
      } 
  
  footnotes <- gtsummary_table_notes(
    bases_info = bases, 
    vars = bases_variables, 
    source_note = source, 
    other_footnotes = footnotes
  )

  if (footnotes != ""){
    tbl <- tbl %>% modify_source_note(
    source_note = footnotes, 
    text_interpret = "html")
  }
  
  return(tbl)

  } else {
    
    helpful_error_message <- paste0("Not working: chk1 = ", chk1,", chk2 = ", chk2)
    return(helpful_error_message)
    
  }

}

writeLines(
  c("multicodes <- ", # Assignment needs to be added manually
  deparse(multicodes)), # Extract function code
  paste0(ddir,"/multicodes.R")) # File name is just the function name 

Examples

Example 1

multicodes(df = ess3, vars = int_loc, source = "European Social Survey")

Location able to access the internet1	%
Home	91.0
Workplace	43.0
On the move	61.3
Some other place	51.0
None of the above	7.3
Refusal	0.0
Don't know	0.0
No answer	0.0
Unweighted bases	1,149
Source: European Social Survey
1 All statistics shown in this table are unweighted.

Example 2

multicodes(df = ess3, vars = int_loc, ci = T, caption = "Multicoded table caption", footnotes = c("This is what footnotes", "Can look like", "<br>See?")) 

Multicoded table caption

Location able to access the internet1	%	95% CI
Home	91.0	[89 - 93]
Workplace	43.0	[40 - 46]
On the move	61.3	[58 - 64]
Some other place	51.0	[48 - 54]
None of the above	7.3	[6 - 9]
Refusal	0.0	[0.0 - 0.4]
Don't know	0.0	[0.0 - 0.4]
No answer	0.0	[0.0 - 0.4]
Unweighted bases	1,149
Abbreviation: CI = Confidence Interval
Footnotes: This is what footnotes Can look like See?
1 All statistics shown in this table are unweighted.

Example 3

multicodes(df = ess3, vars = int_loc, breaks = c("gndr2"), sig =T)

Crosstabulation of Location able to access the internet

Location able to access the internet1	Overall	Gender
		Male	Female	p-value2
Home	91.0	92.7	89.7	0.082
Workplace	43.0	47.0	39.8	0.014
On the move	61.3	65.6	57.9	0.007
Some other place	51.0	52.0	50.2	0.6
None of the above	7.3	5.9	8.4	0.11
Refusal	0.0	0.0	0.0	>0.9
Don't know	0.0	0.0	0.0	>0.9
No answer	0.0	0.0	0.0	>0.9
Unweighted bases	1,149	506	643
1 All statistics shown in this table are unweighted.
2 Pearson’s Chi-squared test; Fisher’s exact test

Example 4

multicodes(df = ess3, vars = int_loc, breaks = c("gndr2"), ci = T)

Crosstabulation of Location able to access the internet

Location able to access the internet1	Overall	95% CI	Gender
			Male	95% CI	Female	95% CI
Home	91.0	[89 - 93]	92.7	[90 - 95]	89.7	[87 - 92]
Workplace	43.0	[40 - 46]	47.0	[43 - 51]	39.8	[36 - 44]
On the move	61.3	[58 - 64]	65.6	[61 - 70]	57.9	[54 - 62]
Some other place	51.0	[48 - 54]	52.0	[48 - 56]	50.2	[46 - 54]
None of the above	7.3	[6 - 9]	5.9	[4 - 8]	8.4	[6 - 11]
Refusal	0.0	[0.0 - 0.4]	0.0	[0.0 - 0.9]	0.0	[0.0 - 0.7]
Don't know	0.0	[0.0 - 0.4]	0.0	[0.0 - 0.9]	0.0	[0.0 - 0.7]
No answer	0.0	[0.0 - 0.4]	0.0	[0.0 - 0.9]	0.0	[0.0 - 0.7]
Unweighted bases	1,149		506		643
Abbreviation: CI = Confidence Interval
1 All statistics shown in this table are unweighted.

Example 5

multicodes(df = ess3, vars = int_loc, breaks = c("gndr2","pray2"))

Crosstabulation of Location able to access the internet

Location able to access the internet1	Overall	Gender		How often pray apart from at religious services
		Male	Female	Every day	More than once a week	Once a week	At least once a month	Only on special holy days	Less often	Never
Home	91.0	92.7	89.7	85.2	96.6	86.0	91.4	100.0	93.2	91.8
Workplace	43.0	47.0	39.8	27.9	42.4	46.0	39.7	41.7	48.8	46.1
On the move	61.3	65.6	57.9	42.1	66.1	48.0	63.8	33.3	69.8	65.4
Some other place	51.0	52.0	50.2	38.8	44.1	50.0	56.9	25.0	53.1	54.7
None of the above	7.3	5.9	8.4	12.6	3.4	10.0	6.9	0.0	5.6	6.6
Refusal	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Don't know	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
No answer	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Unweighted bases	1,149	506	643	183	59	50	58	12	162	622
1 All statistics shown in this table are unweighted.

Example 6

multicodes(df = ess3, vars = int_loc, breaks = c("gndr2","pray2"), ci =T)

Crosstabulation of Location able to access the internet

Location able to access the internet1	Overall	95% CI	Gender				How often pray apart from at religious services
			Male	95% CI	Female	95% CI	Every day	95% CI	More than once a week	95% CI	Once a week	95% CI	At least once a month	95% CI	Only on special holy days	95% CI	Less often	95% CI	Never	95% CI
Home	91.0	[89 - 93]	92.7	[90 - 95]	89.7	[87 - 92]	85.2	[79 - 90]	96.6	[87 - 99]	86.0	[73 - 94]	91.4	[80 - 97]	100.0	[70 - 100]	93.2	[88 - 96]	91.8	[89 - 94]
Workplace	43.0	[40 - 46]	47.0	[43 - 51]	39.8	[36 - 44]	27.9	[22 - 35]	42.4	[30 - 56]	46.0	[32 - 61]	39.7	[27 - 53]	41.7	[16 - 71]	48.8	[41 - 57]	46.1	[42 - 50]
On the move	61.3	[58 - 64]	65.6	[61 - 70]	57.9	[54 - 62]	42.1	[35 - 50]	66.1	[53 - 78]	48.0	[34 - 62]	63.8	[50 - 76]	33.3	[11 - 65]	69.8	[62 - 77]	65.4	[62 - 69]
Some other place	51.0	[48 - 54]	52.0	[48 - 56]	50.2	[46 - 54]	38.8	[32 - 46]	44.1	[31 - 58]	50.0	[37 - 63]	56.9	[43 - 70]	25.0	[7 - 57]	53.1	[45 - 61]	54.7	[51 - 59]
None of the above	7.3	[6 - 9]	5.9	[4 - 8]	8.4	[6 - 11]	12.6	[8 - 18]	3.4	[0.6 - 13]	10.0	[4 - 23]	6.9	[2 - 18]	0.0	[0.0 - 30]	5.6	[3 - 11]	6.6	[5 - 9]
Refusal	0.0	[0.0 - 0.4]	0.0	[0.0 - 0.9]	0.0	[0.0 - 0.7]	0.0	[0.0 - 3]	0.0	[0.0 - 8]	0.0	[0.0 - 9]	0.0	[0.0 - 8]	0.0	[0.0 - 30]	0.0	[0.0 - 3]	0.0	[0.0 - 0.8]
Don't know	0.0	[0.0 - 0.4]	0.0	[0.0 - 0.9]	0.0	[0.0 - 0.7]	0.0	[0.0 - 3]	0.0	[0.0 - 8]	0.0	[0.0 - 9]	0.0	[0.0 - 8]	0.0	[0.0 - 30]	0.0	[0.0 - 3]	0.0	[0.0 - 0.8]
No answer	0.0	[0.0 - 0.4]	0.0	[0.0 - 0.9]	0.0	[0.0 - 0.7]	0.0	[0.0 - 3]	0.0	[0.0 - 8]	0.0	[0.0 - 9]	0.0	[0.0 - 8]	0.0	[0.0 - 30]	0.0	[0.0 - 3]	0.0	[0.0 - 0.8]
Unweighted bases	1,149		506		643		183		59		50		58		12		162		622
Abbreviation: CI = Confidence Interval
1 All statistics shown in this table are unweighted.

multicodes(df = svy_df, vars = int_loc, breaks = c("gndr2","pray2"), ci = F)

Crosstabulation of Location able to access the internet

Location able to access the internet1	Overall	Gender		How often pray apart from at religious services
		Male	Female	Every day	More than once a week	Once a week	At least once a month	Only on special holy days	Less often	Never
Home	94.7	95.5	94.0	90.3	97.6	91.7	95.7	100.0	96.8	95.1
Workplace	52.9	59.1	46.9	35.4	51.8	54.6	42.8	55.4	59.8	56.8
On the move	70.7	75.2	66.3	49.5	74.3	55.0	76.5	49.4	80.0	74.6
Some other place	58.6	58.8	58.4	50.2	47.9	58.7	64.0	38.3	62.1	60.9
None of the above	4.2	3.4	5.0	8.3	2.4	6.6	3.7	0.0	2.5	3.7
Refusal	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Don't know	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
No answer	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
Unweighted bases	1,149	506	643	183	59	50	58	12	162	622
1 All statistics shown in this table are weighted.

Applying base size arguments

int_loc

## [1] "acchome" "accwrk"  "accmove" "accoth"  "accnone" "accref"  "accdk"   "accna"

var_descriptions <- rep("Asked of all respondents as well, but with a separate label for testing", 8)
names(var_descriptions) <- int_loc
var_descriptions

##                                                                   acchome 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accwrk 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accmove 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accoth 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accnone 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accref 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                     accdk 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                     accna 
## "Asked of all respondents as well, but with a separate label for testing"

example_of_base_information <- base_information(
  data = ess3,
  general_base = "Asked of all respondents", 
  specific_bases = var_descriptions
)

map(example_of_base_information, head)

## [[1]]
## [1] "Asked of all respondents"
## 
## [[2]]
##                                                                   acchome 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accwrk 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accmove 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accoth 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                   accnone 
## "Asked of all respondents as well, but with a separate label for testing" 
##                                                                    accref 
## "Asked of all respondents as well, but with a separate label for testing" 
## 
## [[3]]
##                                                   acchome                                                    accwrk 
##              "Location able to access the internet: Home"         "Location able to access the internet: Workplace" 
##                                                   accmove                                                    accoth 
##       "Location able to access the internet: On the move"  "Location able to access the internet: Some other place" 
##                                                   accnone                                                    accref 
## "Location able to access the internet: None of the above"           "Location able to access the internet: Refusal" 
## 
## [[4]]
##                                 name                             essround                              edition 
##                   "Title of dataset"                          "ESS round"                            "Edition" 
##                             proddate                                 idno                                cntry 
##                    "Production date" "Respondent's identification number"                            "Country"

multicodes(df = ess3, vars = int_loc, breaks = c("gndr2"), ci = F, bases = example_of_base_information)

Crosstabulation of Location able to access the internet

Location able to access the internet1	Overall	Gender
		Male	Female
Home	91.0	92.7	89.7
Workplace	43.0	47.0	39.8
On the move	61.3	65.6	57.9
Some other place	51.0	52.0	50.2
None of the above	7.3	5.9	8.4
Refusal	0.0	0.0	0.0
Don't know	0.0	0.0	0.0
No answer	0.0	0.0	0.0
Unweighted bases	1,149	506	643
Variable base descriptions: Asked of all respondents as well, but with a separate label for testing: Location able to access the internet All other variables: Asked of all respondents
1 All statistics shown in this table are unweighted.